The amount of information, as well as the number of goods and services, available to individuals is increasing exponentially. This increase in items and information is occurring across all domains, e.g. sound recordings, restaurants, movies, World Wide Web pages, clothing stores, etc. An individual attempting to find useful information, or to decide between competing goods and services, is often faced with a bewildering selection of sources and choices.
Individual sampling of all items, even in a particular domain, may be impossible. For example, sampling every restaurant of a particular type in New York City would tax even the most avid diner. Such a sampling would most likely be prohibitively expensive to carry out, and the diner would have to suffer through many unenjoyable restaurants.
In many domains, individuals have simply learned to manage information overload by relying on a form of generic referral system. For example, in the domain of movie and sound recordings, many individuals rely on reviews written by paid reviewers. These reviews, however, are simply the viewpoint of one or two individuals and may not have a likelihood of correlating with how the individual will actually perceive the movie or sound recording. Many individuals may rely on a review only to be disappointed when they actually sample the item.
One method of attempting to provide an efficient filtering mechanism is to use content-based filtering. The content-based filter selects items from a domain for the user to sample based upon correlations between the content of the item and the user's preferences. Content-based filtering schemes suffer from the drawback that the items to be selected must be in some machine-readable form, or attributes describing the content of the item must be entered by hand. This makes content-based filtering problematic for existing items such as sound recordings, photographs, art, video, and any other physical item that is not inherently machine-readable. While item attributes can be assigned by hand in order to allow a content-based search, for many domains of items such assignment is not practical. For example, it could take decades to enter even the most rudimentary attributes for all available network television video clips by hand.
Perhaps more importantly, even the best content-based filtering schemes cannot provide an analysis of the quality of a particular item as it would be perceived by a particular user, since quality is inherently subjective. So, while a content-based filtering scheme may select a number of items based on the content of those items, a content-based filtering scheme generally cannot further refine the list of selected items to recommend items that the individual will enjoy.
Co-pending application Ser. No. 08/597,442, filed Feb. 2, 1996, describes a method for recommending an item to a user which begins by storing a user profile in memory for each user. The user profile includes ratings given to items by the user. An item profile is also stored in memory which includes ratings given to the item by users. The profile of each item rated by the user is retrieved from memory and used to determine which other users of the system have rated that item. The profile of each of those users is retrieved from memory and a similarity factor between the initial user each of the users that have rated the item is calculated. The similarity factors are calculated responsive to the retrieved user profiles. A set of neighboring users is selected responsive to the similarity factors and a weight is assigned to each of the neighboring users. The neighboring users and the weights given to them are used together with the ratings given to items by those neighboring users to recommend at least one item to the initial user.
Users may, however, experience some reluctance in rating items if they are not able to control access to their preference information. This is understandable since the preference information entered by the users will have a high degree of personal content. In addition, users may enter demographic information to further aid the recommendation process and this data may be regarded as especially sensitive data. Since entry of preference data is required in order to make recommendations to the user, and entry of demographic and other data is helpful, a system needs to be provided which allows the user to indicate whether he or she desires to allow his or her data to be transmitted to nodes within a distributed system. The user may also desire to specify certain nodes which may receive the data and other nodes which should not receive data.